Intelligent demand side management for optimal energy scheduling of grid connected microgrids

R. Seshu Kumar, L. Phani Raghav, D. Koteswara Raju and Arvind R. Singh

Applied Energy, 2021, vol. 285, issue C, No S0306261921000040

Abstract: The incorporation of renewables and communication technologies to the utility paves a way for self-sustained microgrids (MG). The volatile nature of these resources, uncertainties associated with the time-varying load, and market prices impose the significance of an efficient energy management system (EMS). So far, the MG optimal operation has been referred to optimize the operating costs only. However, the prospects of incorporating demand-side management (DSM) with the EMS problem and its effect on total operating cost and peak reduction is needed to be evaluated. To fill this gap, the impact of utility induced flexible load shaping strategy on non-dispatchable energy sources is investigated in this paper. A three-stage stochastic EMS framework is proposed for solving optimal day-ahead scheduling and minimizing the operational cost of grid-connected MG. In the first stage, four possible scenarios for solar and wind power generation profiles are created to address the uncertainty problem by considering real-time meteorological data. The second stage deals with the MG system configuration, operational constraints, and assigning DSM load participation data to be incorporated with the objective function. In this regard, the Quantum Particle Swarm Optimization is devised at stage three to obtain the optimal power dispatch configuration for DG units, maximizing the power export to the utility and compare the results with and without incorporating DSM participation for all scenarios. The obtained simulation results show the competence of the proposed stochastic framework about cost reduction by 43.81% with the implementation of the load participation level of 20% DSM.

Keywords: Microgrid; Quantum Particle Swarm Optimization; Demand Side Management; Energy Management System; Stochastic optimization (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (23)

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DOI: 10.1016/j.apenergy.2021.116435

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